84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
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# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for preprocessors."""
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from absl.testing import parameterized
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import numpy as np
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from robotics_transformer.film_efficientnet import preprocessors
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from tensor2robot.utils import tensorspec_utils
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import tensorflow.compat.v2 as tf
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def _random_image(shape):
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images = tf.random.uniform(
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shape, minval=0, maxval=255, dtype=tf.dtypes.int32, seed=42)
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return tf.cast(images, tf.uint8)
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def _get_features(
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image_shape=(2, 512, 640, 3), use_task_image=False, use_goal_image=False):
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# Time-dimension stacking occurs during training but not eval.
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state = tensorspec_utils.TensorSpecStruct(image=_random_image(image_shape))
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if use_task_image:
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state.task_image = _random_image(image_shape)
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if use_goal_image:
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state.goal_image = _random_image(image_shape)
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return state
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class PreprocessorsTest(tf.test.TestCase, parameterized.TestCase):
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@parameterized.parameters((True, False, False), (False, True, False),
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(True, False, True), (False, True, True))
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def testConvertDtypeAndCropImages(self, training, pad_then_crop,
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convert_dtype):
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features = _get_features()
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images = preprocessors.convert_dtype_and_crop_images(
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features.image,
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training=training,
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pad_then_crop=pad_then_crop,
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convert_dtype=convert_dtype)
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expected_cropped_shape = ([2, 512, 640, 3]
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if pad_then_crop else [2, 472, 472, 3])
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tf.ensure_shape(images, expected_cropped_shape)
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if convert_dtype:
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self.assertEqual(images.dtype, tf.float32)
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self.assertLessEqual(images.numpy().max(), 1.)
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self.assertGreaterEqual(images.numpy().min(), 0.)
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else:
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self.assertEqual(images.dtype, tf.uint8)
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self.assertLessEqual(images.numpy().max(), 255)
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self.assertGreaterEqual(images.numpy().min(), 0)
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self.assertGreater(images.numpy().max(), 1)
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def testConvertDtypeAndCropImagesSeeded(self):
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features = _get_features()
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seed = tf.constant([1, 2], tf.int32)
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images1 = preprocessors.convert_dtype_and_crop_images(
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features.image, training=True, pad_then_crop=True, seed=seed)
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images2 = preprocessors.convert_dtype_and_crop_images(
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features.image, training=True, pad_then_crop=True, seed=seed)
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diff = np.sum(np.abs(images1.numpy() - images2.numpy()))
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self.assertAlmostEqual(diff, 0)
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def testConvertDtypeAndCropImagesUnseeded(self):
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features = _get_features()
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seed1 = tf.constant([1, 2], tf.int32)
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images1 = preprocessors.convert_dtype_and_crop_images(
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features.image, training=True, pad_then_crop=True, seed=seed1)
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seed2 = tf.constant([2, 3], tf.int32)
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images2 = preprocessors.convert_dtype_and_crop_images(
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features.image, training=True, pad_then_crop=True, seed=seed2)
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diff = np.sum(np.abs(images1.numpy() - images2.numpy()))
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self.assertNotAlmostEqual(diff, 0)
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